Evidence map›Paper›PMID 38099942›Full record

ReviewRice (New York, N.Y.)2023

Realized Genetic Gain in Rice: Achievements from Breeding Programs.

Fallou Seck, Giovanny Covarrubias-Pazaran, Tala Gueye, Jérôme Bartholomé

Abstract readReview
In one paragraph

Review in Rice (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
  2. Review
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  5. Article
  6. Review
  7. 110 years of rice breeding at LSU: realized genetic gains and future optimization.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Article
  8. Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Fallou SeckRice Breeding Innovation Platform, International Rice Research Institute, DAPO Box7777, Metro Manila, Philippines.
Giovanny Covarrubias-PazaranRice Breeding Innovation Platform, International Rice Research Institute, DAPO Box7777, Metro Manila, Philippines.
Tala GueyeUniversity Iba Der Thiam of Thiès, GrandStanding, Thiès, Senegal.
Jérôme BartholoméCIRAD, UMR AGAP, Cali, Colombia. jerome.bartholome@cirad.fr.

Funding

Bill and Melinda Gates Foundation OPP1194925
6 · The paper itself

Abstract

Genetic improvement is crucial for ensuring food security globally. Indeed, plant breeding has contributed significantly to increasing the productivity of major crops, including rice, over the last century. Evaluating the efficiency of breeding strategies necessitates a quantification of this progress. One approach involves assessing the genetic gain achieved through breeding programs based on quantitative traits. This study aims to provide a theoretical understanding of genetic gain, summarize the major results of genetic gain studies in rice breeding, and suggest ways of improving breeding program strategies and future studies on genetic gain. To achieve this, we present the concept of genetic gain and the essential aspects of its estimation. We also provide an extensive literature review of genetic gain studies in rice (Oryza sativa L.) breeding programs to understand the advances made to date. We reviewed 29 studies conducted between 1999 and 2023, covering different regions, traits, periods, and estimation methods. The genetic gain for grain yield, in particular, showed significant variation, ranging from 1.5 to 167.6 kg/ha/year, with a mean value of 36.3 kg/ha/year. This translated into a rate of genetic gain for grain yield ranging from 0.1% to over 3.0%. The impact of multi-trait selection on grain yield was clarified by studies that reported genetic gains for other traits, such as plant height, days to flowering, and grain quality. These findings reveal that while breeding programs have achieved significant gains, further improvements are necessary to meet the growing demand for rice. We also highlight the limitations of these studies, which hinder accurate estimations of genetic gain. In conclusion, we offer suggestions for improving the estimation of genetic gain based on quantitative genetic principles and computer simulations to optimize rice breeding strategies.

Indexed as

Breeder’s equationBreeding programGenetic gainResponse to selectionRice

Identifiers

PMID38099942
PMCPMC10724102

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.